Current Medicinal Chemistry - Volume 31, Issue 40, 2024

Prostate cancer (PCa) is the most frequent and second-lethal cancer among men. Despite considerable efforts to explore treatments like autologous cellular immunotherapy and immune checkpoint inhibitors, their success remains limited. The intricate tumor microenvironment (TME) and its interaction with the immune system pose significant challenges in PCa treatment. Consequently, researchers have directed their focus on augmenting the immune system's anti-tumor response by targeting the STimulator of the Interferon Genes (STING) pathway. The STING pathway is activated when foreign DNA is detected in the cytoplasm of innate immune cells, resulting in the activation of endoplasmic reticulum (ER) STING. This, in turn, triggers an augmentation of signaling, leading to the production of type I interferon (IFN) and other pro-inflammatory cytokines. Numerous studies have demonstrated that activation of the STING pathway induces immune system rejection and targeted elimination of PCa cells. Researchers have been exploring various methods to activate the STING pathway, including the use of bacterial vectors to deliver STING agonists and the combination of radiation therapy with STING agonists. Achieving effective radiation therapy with minimal side effects and optimal anti-tumor immune responses necessitates precise adjustments to radiation dosing and fractionation schedules. This comprehensive review discusses promising findings from studies focusing on activating the STING pathway to combat PCa. The STING pathway exhibits the potential to serve as an effective treatment modality for PCa, offering new hope for improving the lives of those affected by this devastating disease.

Currently, the main therapeutic methods for cancer include surgery, radiation therapy, and chemotherapy. However, chemotherapy still plays an important role in tumor therapy. Due to the variety of pathogenic factors, the development process of tumors is complex and regulated by many factors, and the treatment of a single drug is easy to cause the human body to produce a drug-resistant phenotype to specific drugs and eventually leads to treatment failure. In the process of clinical tumor treatment, the combination of multiple drugs can produce stronger anti-tumor effects by regulating multiple mechanisms and can reduce the problem of tumor drug resistance while reducing the toxic side effects of drugs. Therefore, it is still a great challenge to construct an efficient and accurate screening method that can systematically consider the synergistic anti-tumor effects of multiple drugs. However, anti-tumor drug synergy prediction is of importance in improving cancer treatment outcomes. However, identifying effective drug combinations remains a complex and challenging task. This review provides a comprehensive overview of cancer drug synergy therapy and the application of artificial intelligence (AI) techniques in cancer drug synergy prediction. In addition, we discuss the challenges and perspectives associated with deep learning approaches. In conclusion, the review of the AI techniques' application in cancer drug synergy prediction can further advance our understanding of cancer drug synergy and provide more effective treatment plans and reasonable drug use strategies for clinical guidance.

Siderophores are low molecular weight compounds produced by microorganisms to scavenge iron in iron-deficient environments. Rhodotorulic acid, a natural hydroxamate siderophore, plays a vital role in iron acquisition for fungi and bacteria. As the simplest natural hydroxamate siderophore, it exhibits a high affinity for ferric ions, enabling it to form stable complexes that facilitate iron uptake and transport within microorganisms. This article provides a comprehensive analysis of this hydroxamate siderophore, rhodotorulic acid, its synthesis, physicochemical properties, and biological significance. It also explores its applications in antifungal and plant protection strategies. Insights into RA derivatives reveal distinct biological effects and applications with potential in various fields, from antioxidants to antifungals. Rhodotorulic acid and its derivatives show promise for novel therapies, plant protection strategies, and iron supplementation in agriculture. Understanding their properties could advance science and medicine with sustainable practices.

Edible oils are inevitable requisites in the human diet as they are enriched with essential fatty acids, vitamins, carotenoids, sterols, and other antioxidants. Due to their nutritive value and commercial significance, edible oils have been used for food preparation for many centuries. The use of global consumption of edible oils has dramatically increased throughout the world in the 21^st century owing to their incredible application in all kinds of food preparation. However, a variety of pollutants, such as pesticides, toxic chemicals, heavy metals, and environmental pollution, have contributed to the contamination of edible oils. Furthermore, the benzophenanthridine alkaloids, sanguinarine, dihydrosanguinarine, butter yellow, and other several agents are added intentionally, which are known to cause a number of human diseases. Apart from this, repeated heating and reusing of oils results in trans fats, and lipid peroxidation alters the fatty acid composition, which adversely affects the health of consumers and increases the risk of cardiovascular diseases. Moreover, the prevention of edible oil contamination in human health at various levels is inevitable to ensure consumer safety. Hence, the present review provides an overview of vegetable cooking oils and the health ailments that detection techniques are focused on.

SARS-CoV-2 invades the respiratory tract epithelium and can result in systemic inflammation prior to an infection caused by either bacteria or fungus. COVID-19-associated mucormycosis (CAM) is a serious condition that can occur during the time of the disease due to increased administration of corticosteroids. Various studies have suggested that statins may improve clinical outcomes in COVID-19 patients. According to several preclinical reports, fluvastatin was shown to exert direct and indirect synergistic antifungal activity. Thus, fluvastatin could be considered a potential antifungal agent when no other option is available. Furthermore, in comparison with other statins, fluvastatin exhibits the fewest drug/drug interactions with anti-Mucorales azoles (e.g., isavuconazole and posaconazole), as well as with medicines that are used in solid organ transplant recipients (e.g., cyclosporine) and HIV-positive individuals (e.g., ritonavir); two groups of patients that have a higher risk of infection with Mucorales fungi following a SARS-CoV-2 infection.

Kidney diseases, particularly Acute Kidney Injury (AKI) and Chronic Kidney Disease (CKD), are identified as global public health issues affecting millions of individuals. In addition, the frequency of renal diseases in the population has increased dramatically and rapidly in recent years. Renal disorders have become a significant public health burden. The pathophysiology of renal diseases is significantly connected with renal cell death, including apoptosis, necrosis, necroptosis, ferroptosis, pyroptosis, and autophagy, as is now recognized. Unlike other forms of cell death, pyroptosis is a unique planned cell death (PCD). Scientists have proven that pyroptosis is crucial in developing various disorders, and this phenomenon is gaining increasing attention. It is considered a novel method of inflammatory cell death. Intriguingly, inflammation is among the most significant pathological characteristics of renal disease. This study investigates the effects of pyroptosis on Acute Kidney Injury (AKI), Chronic Kidney Disease (CKD), Diabetic Nephropathy (DN), Immunoglobulin A (IgA) Nephropathy, and Lupus Nephritis (LN) to identify novel therapeutic targets for kidney diseases.

In the field of precision and personalized medicine, the next generation sequencing method has begun to take an active place as genome-wide screening applications in the diagnosis and treatment of diseases. Studies based on the determination of the therapeutic efficacy of personalized drug use in cancer treatment in the size of the transcriptome and its extension, lncRNA, have been increasing rapidly in recent years. Targeting and/or regulating noncoding RNAs (ncRNAs) consisting of long noncoding RNAs (lncRNAs) are promising strategies for cancer treatment. Within the scope of rapidly increasing studies in recent years, it has been shown that many natural agents obtained from biological organisms can potentially alter the expression of many lncRNAs associated with oncogenic functions. Natural agents include effective small molecules that provide anti-cancer effects and have been used as chemotherapy drugs or in combination with standard anti-cancer drugs used in routine treatment. In this review, it was aimed to provide detailed information about the potential of natural agents to regulate and/or target non-coding RNAs and their mechanisms of action to provide an approach for cancer therapy. The discovery of novel anti-cancer targets and subsequent development of effective drugs or combination strategies that are still needed for most cancers will be promising for cancer treatment.

Gastric cancer (GC) is a highly intricate gastrointestinal malignancy. Early detection of gastric cancer forms the cornerstone of precision medicine. Several studies have been conducted to investigate early biomarkers of gastric cancer using genomics, transcriptomics, proteomics, and metabolomics, respectively. However, endogenous substances associated with various omics are concurrently altered during gastric cancer development. Furthermore, environmental exposures and family history can also induce modifications in endogenous substances. Therefore, in this study, we primarily investigated alterations in DNA mutation, DNA methylation, mRNA, lncRNA, miRNA, circRNA, and protein, as well as glucose, amino acid, nucleotide, and lipid metabolism levels in the context of GC development, employing genomics, transcriptomics, proteomics, and metabolomics. Additionally, we elucidate the impact of exposure factors, including HP, EBV, nitrosamines, smoking, alcohol consumption, and family history, on diagnostic biomarkers of gastric cancer. Lastly, we provide a summary of the application of machine learning in integrating multi-omics data. Thus, this review aims to elucidate: i) the biomarkers of gastric cancer related to genomics, transcriptomics, proteomics, and metabolomics; ii) the influence of environmental exposure and family history on multi-omics data; iii) the integrated analysis of multi-omics data using machine learning techniques.